Enhancing Crop Yield Prediction Using Machine Learning and Geospatial Data

Abstract

This study presents a geospatially informed machine learning approach to improve crop yield prediction in Zambia, where agriculture underpins rural livelihoods and national food security. The research integrates satellite-derived vegetation indices, principally the Normalised Difference Vegetation Index (NDVI), with meteorological indicators including seasonal rainfall distribution and temperature trends, across a 25-year wheat yield record (1999 to 2024) for a commercial farm in Chongwe District, Zambia. Datasets were harmonised through spatial standardisation, feature engineering, and temporal aggregation to produce a coherent input structure for a Random Forest regression model benchmarked against Extreme Gradient Boosting (XGBoost). Rainfall frequency, seasonal thermal accumulation, and vegetation vigour emerged as the most influential predictors of yield variability. Random Forest achieved a stable coefficient of determination (R2 = 0.389), while XGBoost exhibited apparent superiority (R2 = 0.950) attributable to overfitting on a small, spatially homogeneous dataset. The findings demonstrate that model selection is critical in small-sample agricultural prediction contexts and contribute an interactive, field-ready decision-support dashboard for precision agriculture in Zambia.